公开(公告)号：US20240078659A1

公开(公告)日：2024-03-07

申请号：US17903262

申请日：2022-09-06

Applicant: Applied Materials Israel Ltd.

Inventor： Yehonatan Hai OFIR ,  Yehonatan RIDELMAN ,  Ran BADANES ,  Boris SHERMAN ,  Boaz COHEN

IPC: G06T7/00 ,  G06T5/00 ,  G06T7/30

CPC classification number: G06T7/001 ,  G06T5/002 ,  G06T5/005 ,  G06T7/30 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30148

Abstract: There is provided a system and method for defect examination on a semiconductor specimen. The method comprises obtaining a runtime image of the semiconductor specimen, generating a reference image based on the runtime image using a machine learning (ML) model, and performing defect examination on the runtime image using the generated reference image. The ML model is previously trained during setup using a training set comprising one or more pairs of training images, each pair including a defective image and a corresponding defect-free image. The training comprises, for each pair, processing the defective image by the ML model to obtain a predicted image, and optimizing the ML model to minimize a difference between the predicted image and the defect-free image.

公开(公告)号：US20240095903A1

公开(公告)日：2024-03-21

申请号：US17947989

申请日：2022-09-19

Applicant: Applied Materials Israel Ltd.

Inventor： Boris SHERMAN ,  Boris LEVANT ,  Ran YACOBY ,  Botser RESHEF ,  Tomer YEMINY

IPC: G06T7/00 ,  G06V10/74 ,  G06V10/75 ,  G06V10/774

CPC classification number: G06T7/001 ,  G06V10/759 ,  G06V10/761 ,  G06V10/774 ,  G06T2207/20081 ,  G06T2207/20224 ,  G06T2207/30148

Abstract: There is provided a system and method for defect examination on a semiconductor specimen. The method comprises obtaining an original image of the semiconductor specimen, the original image having a first region annotated as enclosing a defective feature; specifying a second region in the original image containing the first region, giving rise to a contextual region between the first region and the second region; identifying in a target image of the specimen a set of candidate areas matching the contextual region in accordance with a matching measure; selecting one or more candidate areas from the set of candidate areas; and pasting the first region or part thereof with respect to the one or more candidate areas, giving rise to an augmented target image usable for defect examination on the semiconductor specimen.

公开(公告)号：US20240428396A1

公开(公告)日：2024-12-26

申请号：US18212179

申请日：2023-06-20

Applicant: Applied Materials Israel Ltd.

Inventor： Boris SHERMAN ,  Boris LEVANT ,  Ran YACOBY ,  Bar DUBOVSKI ,  Botser RESHEF ,  Tomer YEMINY ,  Omer GRANOVITER ,  Ran BADANES

IPC: G06T7/00 ,  G06T7/10 ,  G06V10/44 ,  G06V10/70 ,  G06V20/70

Abstract: There is provided a system and method of semiconductor specimen examination. The method includes obtaining a plurality of images of a semiconductor specimen acquired by an examination tool; processing the plurality of images using a first machine learning (ML) model for defect detection, thereby obtaining, from the plurality of images, a set of images labeled with detected defects, wherein the first ML model is previously trained using a first training set comprising a subset of synthetic defective images each containing one or more synthetic defects, and a subset of nominal images; and training a second ML model using a second training set comprising at least part of the set of images labeled with detected defects, wherein the second ML model, upon being trained, is usable for defect detection with improved detection performance with respect to the first ML model.

公开(公告)号：US20240338811A1

公开(公告)日：2024-10-10

申请号：US18130845

申请日：2023-04-04

Applicant: Applied Materials Israel Ltd.

Inventor： Yehonatan Hai OFIR ,  Yotam Nissim BEN SHOSHAN ,  Ran BADANES ,  Boris SHERMAN

IPC: G06T7/00 ,  G06N3/0455

CPC classification number: G06T7/001 ,  G06N3/0455 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: There is provided a system and method of examination a semiconductor specimen. The method includes obtaining a runtime image of the specimen; processing the runtime image using a first machine learning (ML) model to extract a set of runtime features representative of a set of patches in the runtime image; and comparing the set of runtime features with a bank of reference features, giving rise to an anomaly map indicative of one or more defective patches in the runtime image. The bank of reference features is previously generated by obtaining a plurality of synthetic reference images generated by a second ML model based on a plurality of actual images; and processing the plurality of synthetic reference images by the first ML model to extract, for each synthetic reference image, a set of reference features representative thereof, giving rise to the bank of reference features.